Abstract
BackgroundHypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events.ObjectiveIn this study, we aim to develop a deep-learning–based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE).MethodsDomain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models.ResultsWe found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03).ConclusionsDespite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients.
Highlights
An estimated 29.1 million Americans aged 20 years or older have diabetes mellitus [1]
The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03)
Despite the challenges posed by small and highly imbalanced data, our CNN-based Hypoglycemic Events (HYPE) system still achieved a high performance for hypoglycemia detection
Summary
An estimated 29.1 million Americans aged 20 years or older have diabetes mellitus [1]. In older adults, increases the frequency of hypoglycemia, defined as blood glucose less than 70 mg/dL [2]. Treatment-associated hypoglycemia is the third-most common adverse drug event in patients with diabetes mellitus. Severe hypoglycemia, requiring third-party help or with blood glucose below 54 mg/dL, is associated with seizures, coma, and death and results in about 25,000 emergency department visits and 11,000 hospitalizations annually among Medicare patients in the United States [3]. Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events
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